activity
20202026
most citedLearning Reward Machines: A Study in Partially Observable Reinforcement Learning

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

math.OC2026

DD-suite: A cross-platform package to build Decision Diagrams for optimization purposes

Antonia F. Blanco, Margarita Castro, Rodrigo Toro Icarte

Decision diagrams (DDs) have become a powerful tool for discrete optimization, supporting a wide range of algorithms that span cut-generation procedures, decomposition methods, and…

math.OC2026

Beyond Hand-Derived Inequalities: Decision Diagrams for Cut Generation in Binary Polynomial Optimization

Martin Cooper, Margarita Castro

We study cutting-plane generation for binary polynomial optimization (BPO), whose feasible region is the multilinear set of a hypergraph. Strong inequalities for this set---such as…

math.OC2022

Markov Chain-based Policies for Multi-stage Stochastic Integer Linear Programming with an Application to Disaster Relief Logistics

Margarita P. Castro, Merve Bodur, Yongjia Song

We introduce an aggregation framework to address multi-stage stochastic programs with mixed-integer state variables and continuous local variables (MSILPs). Our aggregation framewo…

math.OC2022

Decision Diagrams for Discrete Optimization: A Survey of Recent Advances

Margarita P. Castro, Andre A. Cire, J. Christopher Beck

In the last decade, decision diagrams (DDs) have been the basis for a large array of novel approaches for modeling and solving optimization problems. Many techniques now use DDs as…

cs.LG2021★ 1 cited

Learning Reward Machines: A Study in Partially Observable Reinforcement Learning

Rodrigo Toro Icarte, Ethan Waldie, Toryn Q. Klassen +3

Reinforcement learning (RL) is a central problem in artificial intelligence. This problem consists of defining artificial agents that can learn optimal behaviour by interacting wit…

math.OC2020

A Combinatorial Cut-and-Lift Procedure with an Application to 0-1 Second-Order Conic Programming

Margarita P. Castro, Andre A. Cire, J. Christopher Beck

Cut generation and lifting are key components for the performance of state-of-the-art mathematical programming solvers. This work proposes a new general cut-and-lift procedure that…